ArticleGenetics in medicine : official journal of the American College of Medical Genetics2019
Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis.
Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed, 103 citations in OpenAlex.
- A text mining and ontology-based approach using phenotypes to obtain relevant literature for rare diseases.iScience · 2026Article
- aiDIVA - hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models.NPJ genomic medicine · 2026Article
- RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.Genome medicine · 2026Article
- Reframing AI for Rare Disease Recognition.Research square · 2026Article
- GEN-KnowRD: Reframing AI for Rare Disease Recognition.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Accelerate the discovery of genetic variants in mitochondrial diseases with Variant prIOritization using Latent spAce.Briefings in bioinformatics · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Multi-class machine learning-based classification of SCID-related genetic variants.Immunologic research · 2025Article
- geneEX: An Integrated Phenotype-Driven Algorithm for Rapid Identification of Causative Variants in Monogenic Disorders.Molecular genetics & genomic medicine · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Review
- An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.Human genetics · 2025Article
- A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.NPJ digital medicine · 2025Article
- Leveraging clinical intuition to improve accuracy of phenotype-driven prioritization.Genetics in medicine : official journal of the American College of Medical Genetics · 2025Article
- Benefits and harms associated with the use of AI-related algorithmic decision-making systems by healthcare professionals: a systematic review.The Lancet regional health. Europe · 2025Article
- Rare disease genomics and precision medicine.Genomics & informatics · 2024Review
- Promise and Peril of a Genotype-First Approach to Mendelian Cardiovascular Disease.Journal of the American Heart Association · 2024Review
- Is artificial intelligence for medical professionals serving the patients? : Protocol for a systematic review on patient-relevant benefits and harms of algorithmic decision-making.Systematic reviews · 2024Article
- VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation.Orphanet journal of rare diseases · 2024Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeDespite the successful progress next-generation sequencing technologies has achieved in diagnosing the genetic cause of rare Mendelian diseases, the current diagnostic rate is still far from satisfactory because of heterogeneity, imprecision, and noise in disease phenotype descriptions and insufficient utilization of expert knowledge in clinical genetics. To overcome these difficulties, we present a novel method called Xrare for the prioritization of causative gene variants in rare disease diagnosis.
methodsWe propose a new phenotype similarity scoring method called Emission-Reception Information Content (ERIC), which is highly tolerant of noise and imprecision in clinical phenotypes. We utilize medical genetic domain knowledge by designing genetic features implementing American College of Medical Genetics and Genomics (ACMG) guidelines.
resultsERIC score ranked consistently higher for disease genes than other phenotypic similarity scores in the presence of imprecise and noisy phenotypes. Extensive simulations and real clinical data demonstrated that Xrare outperforms existing alternative methods by 10-40% at various genetic diagnosis scenarios.
conclusionThe Xrare model is learned from a large database of clinical variants, and derives its strength from the tight integration of medical genetics features and phenotypic features similarity scores. Xrare provides the clinical community with a robust and powerful tool for variant prioritization.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.